5-15-19 MERS data

INTRODUCTION

A data set on Middle East Respiratory Sydrome Corona Virus (MERS-CoV), and that has been made available by Andrw Rambaut, was utilized to practice technqiues in R studio. As such, this document contains various practce plots.

MATERIALS AND METHODS

This project used R-studio for statistical analysis.

STEP ONE: Load Libaries

library(lubridate)

## 
## Attaching package: 'lubridate'

## The following object is masked from 'package:base':
## 
##     date

library(ggplot2)
library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

STEP TWO: Functions

No functions were used in this analysis of the MERS-CoV data.

STEP THREE: Data Loading

setwd('C:/Users/msb75462/Desktop/mers')
mers<-read.csv('cases.csv')

STEP FOUR: Data Analysis

mers$hospitalized[890]<-c('2015-02-20')

mers<-mers[-471,]

mers$hospitalized2<-ymd(mers$hospitalized)

## Warning: 5 failed to parse.

mers$onset2<-ymd(mers$onset)

class(mers$onset2)

## [1] "Date"

day0<-min(na.omit(mers$onset2))

mers$epi.day<-as.numeric(mers$onset2-day0)

Endimic curves

ggplot(data=mers) +
  geom_bar(mapping=aes(x=epi.day, fill=country)) +
  labs(x='Epidemic day', y='Case count', title='Global count of MERS cases by date of symptom onset',
       caption="Data from: https://github.com/rambaut/MERS-Cases/blob/gh-pages/data/cases.csv")

## Warning: Removed 535 rows containing non-finite values (stat_count).

## Warning: position_stack requires non-overlapping x intervals

This plots shows us the Global count of MERS cases by date with a legend of countries on the side.

ggplot(data=mers) +
  geom_bar(mapping=aes(x=epi.day, fill=gender)) +
  labs(x='Epidemic day', y='Case count', title='Global count of MERS cases by date of symptom onset',
       caption="Data from: https://github.com/rambaut/MERS-Cases/blob/gh-pages/data/cases.csv")

## Warning: Removed 535 rows containing non-finite values (stat_count).

This plots shows us the Global count of MERS cases by date with a legend coordiateing to the male and female genders on the side.

Univariate Plot

mers$infectious.period <- mers$hospitalized2-mers$onset2

This is the raw infectious period.

Histogram

class(mers$infectious.period)

## [1] "difftime"

mers$infectious.period <- as.numeric(mers$infectious.period, units = "days")
ggplot(data=mers) +geom_histogram(aes(x=infectious.period)) +
  labs(x='Infectious period', y='Frequency', title='Distribution of calculated MERS infectious period',
       caption="Data from: https://github.com/rambaut/MERS-Cases/blob/gh-pages/data/cases.csv")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 727 rows containing non-finite values (stat_bin).

Nonsocimal Infection

mers$infectious.period2 <- ifelse(mers$infectious.period<0,0,mers$infectious.period)
ggplot(data=mers) + geom_histogram(aes(x=infectious.period2)) +
  labs(x='Infectious period', y='Frequency', title='Distribution of calculated MERS infectious period',
      caption="Data from: https://github.com/rambaut/MERS-Cases/blob/gh-pages/data/cases.csv")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 727 rows containing non-finite values (stat_bin).

This is an updated infectious period as this is a nonsocimal infection.

Density Plot

ggplot(data=mers) +
  geom_density(mapping=aes(x=infectious.period2)) +
  labs(x='Infectious period', y='Frequency',
       title='Probability density for MERS infectious period (positive values only)', caption ="Data from: https://github.com/rambaut/MERS-Cases/blob/gh-pages/data/cases.csv")

## Warning: Removed 727 rows containing non-finite values (stat_density).

Area Plot

ggplot(data=mers) +
  geom_area(stat='bin', mapping=aes(x=infectious.period2)) +
  labs(x='Infectious period', y='Frequency',
       title='Area plot for MERS infectious period (positive values only)', caption ="Data from: https://github.com/rambaut/MERS-Cases/blob/gh-pages/data/cases.csv")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 727 rows containing non-finite values (stat_bin).

Bivariate Plot

ggplot(data=mers, mapping=aes(x=epi.day, y=infectious.period2)) +
  geom_smooth()+
  geom_point() +
  labs(x='Epidemic day', y='Infectious period',
       title='MERS infectious period (positive values only) over time', caption="Data from: https://github.com/rambaut/MERS-Cases/blob/gh-pages/data/cases.csv")

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## Warning: Removed 727 rows containing non-finite values (stat_smooth).

## Warning: Removed 727 rows containing missing values (geom_point).

Faceting - Multipanel Plot

ggplot(data=mers, mapping=aes(x=epi.day, y=infectious.period2)) +
  geom_point(mapping = aes(color=country)) +
  facet_wrap(~ country) +
  scale_y_continuous(limits = c(0, 50)) +
  labs(x='Epidemic day', y='Infectious period',
  title='MERS infectious period (positive values only) over time', caption="Data from: https://github.com/rambaut/MERS-Cases/blob/gh-pages/data/cases.csv")

## Warning: Removed 728 rows containing missing values (geom_point).

Seperate Plots for Male and Female

ggplot(data=subset(mers, gender %in% c('M', 'F') &
                     country %in% c('KSA', 'Oman', 'Iran', 'Jordan', 'Qatar', 'South Korea','UAE')),
       mapping=aes(x=epi.day, y=infectious.period2)) +
  geom_point(mapping = aes(color=country)) +
  facet_grid(gender ~ country) +
  scale_y_continuous(limits = c(0, 50)) +
  labs(x='Epidemic day', y='Infectious period',
       title='MERS infectious period by gender and country',
       caption="Data from: https://github.com/rambaut/MERS-Cases/blob/gh-pages/data/cases.csv")

## Warning: Removed 692 rows containing missing values (geom_point).

Interactive Plot

epi.curve <- ggplot(data=mers) +
geom_bar(mapping=aes(x=epi.day)) +
labs(x='Epidemic day', y='Case count', title='Global count of MERS cases by date of symptom onset',
caption="Data from: https://github.com/rambaut/MERS-Cases/blob/gh-pages/data/cases.csv")
ggplotly(epi.curve)

## Warning: Removed 535 rows containing non-finite values (stat_count).

Results

This study reflected………

Conclusion